Search results for: weed infestation forecast

Authors: Petra Gajdačová

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The presented research deals with a complex evaluation of after-harvest remnants utilization for particleboard production. Agricultural crops that are in the Czech Republic widely grown are in the scope of interest. Researches dealing with composites from agricultural rests solved mostly physical and mechanical properties of produced materials. For the commercialization of these results, however, one another step is essential. It is needed to evaluate the composites production from agricultural rests more comprehensive, take into account all aspects that affect their production, not only material characteristics of produced composites. In this study, descriptive, comparative and synthesis methods were used. Results of this research include a supply stability forecast, technical and technological differences of production of particleboards from agricultural rests and quantification of an economical potential of the agricultural rests.

Keywords: agricultural crops, annual plant, composite material, particleboard

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Authors: Sofia Ayouche, Rachid Ellaia, Rajae Aboulaich

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Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve.

Keywords: optimization, zero-coupon curve, Nelson-Siegel-Svensson, particle swarm optimization, Nelder-Mead algorithm

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Authors: G. L. C. Yap

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Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process. The Gaussianity assumption simplifies the parameter estimation using the Whittle approximation. Nonetheless, this assumption may not be met in the finite samples and there may be a need to normalize the financial series. Based on the empirical indices S&P500 and DAX, this paper examines the performance of the linear volatility model pre-treated with normalization compared to its existing counterpart. The empirical results show that by including normalization as a pre-treatment procedure, the forecast performance outperforms the existing model in terms of statistical and economic evaluations.

Keywords: Gaussian process, long-memory, normalization, value-at-risk, volatility, Whittle estimator

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Authors: Juliana Sagretti, Susy Sabato

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Despite the poverty in the world, a third of all food produced in the world is wasted. FAO, the United Nations Organization of Agriculture and Food, points out the need to combine actions and new technologies to combat hunger and waste in contrast to the high production of food in the world. The energy of ionizing radiation in food brought many positive results, such as increased validity and insect infestation control. The food banks are organizations that act at various points of food chain to collect and distribute food to the needy. So, the aim of this study was to initiate a partnership between irradiation and the food bank through the development of a questionnaire to evaluate and disseminate the knowledge and acceptance of individuals in the food bank in Brazil. In addition, this study aimed to standardize a basis questionnaire for future research assessment of irradiated foods. For the construction of the questionnaire as a measuring instrument, a comprehensive and rigorous literature review was made. Its covered qualitative research, questionnaires, sensory evaluation and food irradiated. Three stages of pre - tests were necessary and related fields of experts were consulted. As a result, the questionnaire has three parts, personal issues, assertive issues and questions of multiple choices and finally an informative question. The questionnaire was applied in Ceagesp food bank in the biggest center of food in Brazil (data not shown).

Keywords: food bank, food irradiation, food waste, sustainability

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Authors: R. O. Anyasi, H. I. Atagana

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In this study, phytoextraction ability of a weed on Aroclor 1254 was studied under greenhouse conditions. Chromolaena odorata plants were transplanted into soil containing 100, 200, and 500 ppm of Aroclor in 1L pots. The experiments were watered daily at 70 % moisture field capacity. Parameters such as fully expanded leaves per plant, shoot length, leaf chlorophyll content as well as root length at harvest were measured. PCB was not phytotoxic to C. odorata growth but plants in the 500 ppm treatment only showed diminished growth at the sixth week. Percentage increases in height of plant were 45.9, 39.4 and 40.0 for 100, 200 and 500 ppm treatments respectively. Such decreases were observed in the leaf numbers, root length and leaf chlorophyll concentration. The control sample showed 48.3 % increase in plant height which was not significant from the treated samples, an indication that C. odorata could survive such PCB concentration and could be used to remediate contaminated soil. Mean total PCB absorbed by C. odorata plant was between 6.40 and 64.60 ppm per kilogram of soil, leading to percentage PCB absorption of 0.03 and 17.03 % per kilogram of contaminated soil. PCBs were found mostly in the root tissues of the plants, and the Bioaccumulation factor were between 0.006-0.38. Total PCB absorbed by the plant increases as the concentration of the compound is increased. With these high BAF ensured, C. odorata could serve as a promising candidate plant in phytoextraction of PCB from a PCB-contaminated soil.

Keywords: phytoremediation, bioremediation, soil restoration, polychlorinated biphenyls (PCB), biological treatment, aroclor

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Razieh Khalafi

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Myocardial infarction (MI) or acute myocardial infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analyzing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behavior of these signals were checked. Results shows this methodology can forecast the ECG and accordingly heart attack with high accuracy.

Keywords: heart attack, ECG, random walk, correlation dimension, forecasting

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Authors: Afaneen Anwer, Samara M. Kamil

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Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load.

Keywords: load forecasting, neural network, back-propagation algorithm, Iraqi power system

Procedia PDF Downloads 551

Authors: Razi Khalafi

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Myocardial Infarction (MI) or acute Myocardial Infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analysing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behaviour of these signals were checked. Results show this methodology can forecast the ECG and accordingly heart attack with high accuracy.

Keywords: heart attack, ECG, random walk, correlation dimension, forecasting

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Authors: Mohammad Ranjbaran Madiseh

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In this research, an experimental apparatus has been developed for observing interfacial stability and deformation of multilayer pressure-driven channel flows. The interface instability of the co-extrusion flow of polyethylene and polypropylene is studied experimentally in a slit geometry. By investigating the growing interfacial wave (IW) and tensile stress of extrudate samples, a relationship between interfacial instability (II) and mechanical properties of polypropylene (PP) and high-density polyethylene (HDPE) has been established. It is shown that the mechanism of interfacial strength is related to interfacial instabilities as well as interfacial strength. It is shown that there is an ability to forecast the quality of final products in the co-extrusion process. In this study, it is found that the instability is controlled by its dominant wave number, which is associated with maximum tensile stress at the interface.

Keywords: interfacial instability, interfacial strength, wave number, interfacial wave

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Authors: Jadwiga Królikowska, Andrzej Królikowski, Jarosław Bajer

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The work delineates the threats of maladjustment of the capacity of rain canals, designed and built in the early 20th century, in connection to heavy rainfall, especially in summer. This is the cause of the so called 'urban floods.' It directly relates to fierce raise of paving in the cities. Resolving this problem requires a change in philosophy of draining the rainfall by wider use of retention, infiltration and usage of rainwater. In systemic approach to managing the safety of urban drainage systems the risk, which is directly connected to safety failures, has been accepted as a measure. The risk level defines the probability of occurrence of losses greater than the ones forecast for a given time frame. The procedure of risk modelling, enabling its numeric analysis by using appropriate weights, is a significant issue in this paper.

Keywords: risk management, drainage system, urban areas, urban floods

Procedia PDF Downloads 335

Authors: Jadwiga Królikowska, Andrzej Królikowski, Jarosław Bajer

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The work delineates the threats of maladjustment of the capacity of rain canals, designed and built in the early 20th century, in connection to heavy rainfall, especially in summer. This is the cause of the so called 'urban floods.' It directly relates to fierce raise of paving in the cities. Resolving this problem requires a change in philosophy of draining the rainfall by wider use of retention, infiltration and usage of rainwater. In systemic approach to managing the safety of urban drainage systems the risk, which is directly connected to safety failures, has been accepted as a measure. The risk level defines the probability of occurrence of losses grater than the ones forecast for a given time frame. The procedure of risk modelling, enabling its numeric analysis by using appropriate weights, is a significant issue in this paper.

Keywords: drainage system, urban areas, risk measurement, systemic approach

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Authors: Katja Ignatieva, Patrick Wong

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We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices.

Keywords: stochastic volatility, affine jump-diffusion models, high frequency data, model specification, markov chain monte carlo

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Authors: Fong-Zuo Lee, Jihn-Sung Lai, Yung-Bin Lin, Xiaoqin Liu, Kuo-Chun Chang, Zhi-Xian Yang, Wen-Dar Guo, Jian-Hao Hong

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In recent years, climate change is a major factor to increase rainfall intensity and extreme rainfall frequency. The increased rainfall intensity and extreme rainfall frequency will increase the probability of flash flood with abundant sediment transport in a river basin. The floods caused by heavy rainfall may cause damages to the bridge, embankment, hydraulic works, and the other disasters. Therefore, the foundation scouring of bridge pier, embankment and spur dike caused by floods has been a severe problem in the worldwide. This severe problem has happened in many East Asian countries such as Taiwan and Japan because of these areas are suffered in typhoons, earthquakes, and flood events every year. Results from the complex interaction between fluid flow patterns caused by hydraulic works and the sediment transportation leading to the formation of river morphology, it is extremely difficult to develop a reliable and durable sensor to measure river bed degradation and bridge pier scouring. Therefore, an innovative scour monitoring sensor using vibration-based Micro-Electro Mechanical Systems (MEMS) was developed. This vibration-based MEMS sensor was packaged inside a stainless sphere with the proper protection of the full-filled resin, which can measure free vibration signals to detect scouring/deposition processes at the bridge pier. In addition, a friendly operational system includes rainfall runoff model, one-dimensional and two-dimensional numerical model, and the applicability of sediment transport equation and local scour formulas of bridge pier are included in this research. The friendly operational system carries out the simulation results of flood events that includes the elevation changes of river bed erosion near the specified bridge pier and the erosion depth around bridge piers. In addition, the system is developed with easy operation and integrated interface, the system can supplies users to calibrate and verify numerical model and display simulation results through the interface comparing to the scour monitoring sensors. To achieve the forecast of the erosion depth of river bed and main bridge pier in the study area, the system also connects the rainfall forecast data from Taiwan Typhoon and Flood Research Institute. The results can be provided available information for the management unit of river and bridge engineering in advance.

Keywords: flash flood, river bed degradation, bridge pier scouring, a friendly operational system

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Authors: M. Lloret-Climent, J. Nescolarde-Selva

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The aim of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on a pre-designed algorithm and applying our interpretation of chaos theory developed in the context of General Systems Theory. This article sets out the causal relationships associated with tourist flows in order to enable the formulation of appropriate strategies. Our results can be applied to numerous cases. For example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups and to analyse tourist behaviour in terms of the most relevant variables. Unlike statistical analyses that merely provide information on current data, our method uses orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

Keywords: attractor, invariant set, tourist flows, orbits, social responsibility, tourism, tourist variables

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Anastasia K. Eleftheriadou, Athanasios Sfetsos, Nikolaos Gounaris

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The present work examines the suitability of a seasonal forecasting model downscaled with a very high spatial resolution in order to assess the energy performance and requirements of buildings. The application of the developed model is applied on Greece for a period and with a forecast horizon of 5 months in the future. Greece, as a country in the middle of a financial crisis and facing serious societal challenges, is also very sensitive to climate changes. The commonly used method for the correlation of climate change with the buildings energy consumption is the concept of Degree Days (DD). This method can be applied to heating and cooling systems for a better management of environmental, economic and energy crisis, and can be used as medium (3-6 months) planning tools in order to predict the building needs and country’s requirements for residential energy use.

Keywords: downscaled seasonal models, degree days, energy performance

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Authors: Priyanka Sharma, Tarkewshwar Dubey, Hemalatha Siva

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Hemorrhoids are one of the most common anorectal diseases around the world. Severalfactors are involved in causing hemorrhoids including irregularbowel function (constipation, diarrhea), exercise, gravity, low fiberdiet, pregnancy, obesity, high abdominal pressure, prolongedsitting, genetic factors, and aging. Pain, bleeding, itching,swelling and anal discharge are the symptoms of the disease. Due to limitedmodern pharmacotherapeutic options available for treatment, theherbal medicines remain the choice of therapy. Blumea lacera (Burm f.) DC. belonging to the Asteraceae family is a common plain land weed of Bangladesh. Traditionally it has been used for treatment of hemorrhoids.Considering the above fact, present study was aimed to validate the ethnomedicinal use of B. lacera leaves on experimental hemorrhoids in rats. The anti-hemorrhoid activity was performed by using croton oil induced rat models. The parameters studied were assessment of TNF-α and IL-6, Evans blue exudation, macroscopic severity score, recto-anal coefficient, histomorphological scores. Also, in vivo antioxidant parameters and histopathological studies were also performed. All paramaters exhibited significant anti-hemorrhoid activity. Moreover ethanolic extract of B. lacera (EBL) leaves 400mg/kg showed ameliorative effect oncroton oil induced hemorrhoids.In conclusion, EBL exhibitedbeneficial effect on croton oil- induced hemorrhoids and validates its ethnomedicinal use in treatment of piles.

Keywords: haemorrhoids, IL-6, piles, TNF-α

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Authors: Nikolaos Reppas, Yilin Gui, Ben Wetenhall, Colin Davie

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Injection of CO2 inside wellbore can induce different kind of loadings that can lead to thermal, hydraulic, and mechanical changes on the surrounding rock. A dual-porosity theoretical constitutive model will be presented for the stability analysis of the wellbore during CO2 injection. An elastoplastic damage response will be considered. A bounding yield surface will be presented considering damage effects on sandstone. The main target of the research paper is to present a theoretical constitutive model that can help industries to safely store CO2 in geological rock formations and forecast any changes on the surrounding rock of the wellbore. The fully coupled elasto-plastic damage Thermo-Hydraulic-Mechanical theoretical model will be validated from existing experimental data for sandstone after simulating some scenarios by using FEM on MATLAB software.

Keywords: carbon capture and storage, rock mechanics, THM effects on rock, constitutive model

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: Yuzhi Cai

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In this paper, we introduce a newly developed quantile function model that can be used for estimating conditional distributions of financial returns and for obtaining multi-step ahead out-of-sample predictive distributions of financial returns. Since we forecast the whole conditional distributions, any predictive quantity of interest about the future financial returns can be obtained simply as a by-product of the method. We also show an application of the model to the daily closing prices of Dow Jones Industrial Average (DJIA) series over the period from 2 January 2004 - 8 October 2010. We obtained the predictive distributions up to 15 days ahead for the DJIA returns, which were further compared with the actually observed returns and those predicted from an AR-GARCH model. The results show that the new model can capture the main features of financial returns and provide a better fitted model together with improved mean forecasts compared with conventional methods. We hope this talk will help audience to see that this new model has the potential to be very useful in practice.

Keywords: DJIA, financial returns, predictive distribution, quantile function model

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Authors: Abdul Wakeel, Hafeez Ur Rehman, Muhammad Umair Mubarak

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Rice is the second most important staple food in Pakistan after wheat. It is not only a healthy food for the people of all age groups but also a source of foreign exchange for Pakistan. Instead of bright history for Basmati rice production, we are suffering from multiple problems reducing yield and quality as well. Rice lodging and water shortage for an-aerobic rice production system is among major glitches of it. Due to water shortage an-aerobic rice production system has to be supplemented or replaced by aerobic rice system. Aerobic rice system has been adopted for production of non-basmati rice in many parts of the world. Also for basmati rice, significant efforts have been made for aerobic rice production, however still has to be improved for effective recommendations. Among two major issues for aerobic rice, weed elimination has been solved to great extent by introducing suitable herbicides, however, low yield production due weak grains and panicle sterility is still elusive. It has been reported that potassium (K) has significant role to decrease panicle sterility in cereals. Potassium deficiency is obvious for rice under aerobic rice production system due to lack of K gradient coming with irrigation water and lowered indigenous K release from soils. Therefore it was hypothesized that K application under aerobic rice production system may improve the rice yield by decreasing panicle sterility. Results from pot and field experiments confirm that application of K fertilizer significantly increased the rice grain yield due to decreased panicle sterility and improving grain health. The quality of rice was also improved by K fertilization.

Keywords: DSR, Basmati rice, aerobic, potassium

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Authors: Olga Sukhoveeva

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Carbon dioxide (CO2) is the main component of carbon biogeochemical cycle and one of the most important greenhouse gases (GHG). Agriculture, particularly arable soils, are one the largest sources of GHG emission for the atmosphere including CO2.Models may be used for estimation of GHG emission from agriculture if they can be adapted for different countries conditions. The only model used in officially at national level in United Kingdom and China for this purpose is DNDC (DeNitrification-DeComposition). In our research, the model DNDC is offered for estimation of GHG emission from arable soils in Russia. The aim of our research was to create the method of DNDC using for evaluation of CO2 emission in Russia based on official statistical information. The target territory was European part of Russia where many field experiments are located. At the first step of research the database on climate, soil and cropping characteristics for the target region from governmental, statistical, and literature sources were created. All-Russia Research Institute of Hydrometeorological Information – World Data Centre provides open daily data about average meteorological and climatic conditions. It must be calculated spatial average values of maximum and minimum air temperature and precipitation over the region. Spatial average values of soil characteristics (soil texture, bulk density, pH, soil organic carbon content) can be determined on the base of Union state register of soil recourses of Russia. Cropping technologies are published by agricultural research institutes and departments. We offer to define cropping system parameters (annual information about crop yields, amount and types of fertilizers and manure) on the base of the Federal State Statistics Service data. Content of carbon in plant biomass may be calculated via formulas developed and published by Ministry of Natural Resources and Environment of the Russian Federation. At the second step CO2 emission from soil in this region were calculated by DNDC. Modelling data were compared with empirical and literature data and good results were obtained, modelled values were equivalent to the measured ones. It was revealed that the DNDC model may be used to evaluate and forecast the CO2 emission from arable soils in Russia based on the official statistical information. Also, it can be used for creation of the program for decreasing GHG emission from arable soils to the atmosphere. Financial Support: fundamental scientific researching theme 0148-2014-0005 No 01201352499 ‘Solution of fundamental problems of analysis and forecast of Earth climatic system condition’ for 2014-2020; fundamental research program of Presidium of RAS No 51 ‘Climate change: causes, risks, consequences, problems of adaptation and regulation’ for 2018-2020.

Keywords: arable soils, carbon dioxide emission, DNDC model, European Russia

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Authors: Jun-Hyeon Cho, Ji-Yoon Lee, Young-Bo Sohn, Dong-Jin Shin, You-Chun Song, Dong-Soo Park, Min-Hee Nam, Young-Up Kwon

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Seed germination is a main factor in japonica rice cultivation. For japonica strains unlike indica lines, fast loss of germination ability during storage leads to risk of seeding and deterioration in the quality. To resolve these problems, germplasms screening for longevity was conducted using six days of compulsory aging stress of high temperature (50℃) and humidity (~95% RH). ‘Dharial’, a weedy rice collected in Bangladesh, was chosen as a source of seed longevity for long term storage. The strong germination trait originated from ‘Dharial’ was incorporated into Korean elite japonica cultivars, ‘Ilmi’ and ‘Gopum’, through backcross method. The germination ratio was evaluated after two years of room temperature storage conditions. A high germination ratio of 80.5% in donor plant of ‘Dharial’ and 77.3% in an introgression line were observed based on the two years of storage while the recurrent japonica cultivars, ‘Ilmi’ and ‘Gopum’, were failed in germination. As a result, we investigated the changes of quality affected by germination ability during storage. A gentle slope of palatability which is one of the measurement items for indirect selection indicator of high eating quality in japonica varieties was studied in a high germination ratio introgression line during storage. The introgression line could be useful to increase longevity and quality of japonica rice seed if molecular breeding strategy such as QTLs analysis is combined.

Keywords: rice, longevity, germination, storage

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Authors: Taner Izzet Acarer, Ece Ceylan Baba

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This paper examines green building certification systems and their current processes in comparison with anticipatory systems. Rapid growth of human population and depletion of natural resources are causing irreparable damage to urban and natural environment. In this context, the concept of ‘sustainable architecture’ has emerged in the 20th century so as to establish and maintain standards for livable urban spaces, to improve quality of urban life, and to preserve natural resources for future generations. The construction industry is responsible for a large part of the resource consumption and it is believed that the ‘green building’ designs that emerge in construction industry can reduce environmental problems and contribute to sustainable development around the world. A building must meet a specific set of criteria, set forth through various certification systems, in order to be eligible for designation as a green building. It is disputable whether methods used by green building certification systems today truly serve the purposes of creating a sustainable world. Accordingly, this study will investigate the sets of rating systems used by the most popular green building certification programs, including LEED (Leadership in Energy and Environmental Design), BREEAM (Building Research Establishment's Environmental Assessment Methods), DGNB (Deutsche Gesellschaft für Nachhaltiges Bauen System), in terms of ‘Anticipatory Systems’ in accordance with the certification processes and their goals, while discussing their contribution to architecture. The basic methodology of the study is as follows. Firstly analyzes of brief historical and literature review of green buildings and certificate systems will be stated. Secondly, processes of green building certificate systems will be disputed by the help of anticipatory systems. Anticipatory Systems is a set of systems designed to generate action-oriented projections and to forecast potential side effects using the most current data. Anticipatory Systems pull the future into the present and take action based on future predictions. Although they do not have a claim to see into the future, they can provide foresight data. When shaping the foresight data, Anticipatory Systems use feedforward instead of feedback, enabling them to forecast the system’s behavior and potential side effects by establishing a correlation between the system’s present/past behavior and projected results. This study indicates the goals and current status of LEED, BREEAM and DGNB rating systems that created by using the feedback technique will be examined and presented in a chart. In addition, by examining these rating systems with the anticipatory system that using the feedforward method, the negative influences of the potential side effects on the purpose and current status of the rating systems will be shown in another chart. By comparing the two obtained data, the findings will be shown that rating systems are used for different goals than the purposes they are aiming for. In conclusion, the side effects of green building certification systems will be stated by using anticipatory system models.

Keywords: anticipatory systems, BREEAM, certificate systems, DGNB, green buildings, LEED

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Authors: Ibrahim Nasiru Dole, Audu Abdullahi, Dike Michiel Chidozie, Lawal Mansur

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Bambara groundnut is an important grain legume and the seeds in storage suffer infestation by Callosobruchus subinnotatus. Laboratory study was conducted to evaluate the efficacy of raw diatomaceous earth (RDE) and plant powders (Jatropha curcas (L.), Eucalyptus camaldulensis (Dehnh.) and Melia azedarach (L.) against C. subinnotatus infesting stored bambara groundnut seeds. Rearing of the insects and the experiments were conducted in Agricultural Biology Laboratory of the Usmanu Danfodiyo University, Sokoto - Nigeria under ambient conditions (29-33oC and a relative humidity of 44-56%). Four treatments at three levels: RDE at 0.5, 1.0 and 1.5 g while plant powders at 0.5, 1.0 and 2.0 g, standard/check (2.0 g of Actellic dust), and a control. These were separately admixed with 100 g of sterilized seeds in glass jars. Each jar was later infested with thirty, 1-2-days old C. subinnotatus of mixed sexes. Adult mortality was assessed 24, 48, 72 and 96 hours, F1 and F2 progenies, seed damage, weight loss and viability were also assessed after 90 days. Eighty-nine (89%) percent adult mortality was recorded in the highest dose of RDE after 96 hours of exposure. These treatments significantly (P < 0.05) suppressed F1 and F2 progenies emergence in relation to the control. The control suffered significantly (P < 0.05) higher seed damage (51.0 %) and weight loss (40.8%) thereby recording lower seed germination. Therefore, RDE and plant powders could be used against C. subinnotatus on stored bambara groundnut seeds.

Keywords: bambara, callosobruchus subinnotatus, plant powders, raw diatomaceous earth,

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: cutting condition, surface roughness, decision tree, CART algorithm

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Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

Abstract:

The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

Procedia PDF Downloads 357

Authors: Md. Ruhul Amin, Shakura Namni, Md. Ramiz Uddin Miah, Md. Giashuddin Miah, Mohammad Zakaria, Sang Jae Suh, Yong Jung Kwon

Abstract:

Insect species abundance and diversity associated with meteorological factors during January to June 2013 at a mango-based agroforestry research field in Bangladesh, and the effects of pests and pollinator species on mango are presented in this study. Among the collected and identified insects, nine species belong to 3 orders were found as pollinator, 11 species in 5 orders as pest, and 13 species in 6 orders as predator. The mango hopper, fruit fly and stone weevil appeared as major pest because of their high levels of abundance and infestation. The hoppers caused 100% inflorescence damage followed by fruit fly (51.7% fruit) and stone weevil (31.0% mature fruit). The major pests exerted significantly higher abundance compared to pollinator, predator and minor pests. Hemipteroid insects were most abundant (60%) followed by Diptera (21%), Hymenoptera (10%), Lepidoptera (5%), and Coleoptera (4%). Insect population increased with increasing trend of temperature and humidity, and revealed peak abundance during April-May. The flower visiting insects differed in their landing duration and showed preference to forage with time of a day. Their foraging activity was found to be peaked between 11.00 am to 01.00 pm. The activity of the pollinators led to higher level of fruit set. This study provides baseline information about the phenological patterns of insect abundance in an agroforestry research field which could be an indication to incorporate some aspects of pest management.

Keywords: agroforestry, abundance, abiotic factors, insects, mango

Procedia PDF Downloads 409